Performance of Different Data Mining Methods for Predicting Rainfall of Rajshahi District, Bangladesh

Md. Mostafizur Rahman (), Md. Abdul Khalek and M. Sayedur Rahman ()
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Md. Mostafizur Rahman: University of Rajshahi, Environment and Data Mining Research Group, Department of Statistics
Md. Abdul Khalek: University of Rajshahi, Environment and Data Mining Research Group, Department of Statistics
M. Sayedur Rahman: University of Rajshahi, Environment and Data Mining Research Group, Department of Statistics

A chapter in Data Science and SDGs, 2021, pp 67-78 from Springer

Abstract: Abstract Rainfall predicting by efficient method is always interesting for particular region because timely and accurately forecasted rainfall data is extremely helpful to take necessary safety action in advance, in case of agricultural production, flood management, drought monitoring, and ongoing construction project. Data mining technique is suitable for predicting different environmental attributes by extracting new relationships from the past data. So, researchers are always trying to predict rainfall data with maximum accuracy by optimizing and integrating different data mining techniques for different weather stations. In our study, we compare the forecasting performance of Linear Discriminant Analysis, Classification and Regression Trees, Random Forest, K-Nearest Neighbors, and Support Vector Machine for rainfall prediction, in case of Rajshahi district, Bangladesh. The monthly time series data for the time period January, 1964 to December, 2017 is considered for analysis. Data mining processes such as data collection, data pre-processing, modeling, and evaluation are strictly followed for empirical studies. The forecasting performances of these models are confirmed by precision, recall, f-measure, and overall accuracy, and also by graphical method. The empirical result showed that the k-nn method is the most suitable method for predicting rainfall in case of Rajshahi district, Bangladesh for the subsequent time period.

Keywords: Linear discriminant analysis; Classification and regression trees; Random forest; k-nearest neighbor; Support vector machine; Rainfall (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_6

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DOI: 10.1007/978-981-16-1919-9_6

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